Method and apparatus for interference cancellation in a rake receiver

ABSTRACT

Systems and methods for despreading received spread spectrum signals are described. Despreading can be performed using both channel estimates and impairment correlation estimates. Techniques for selecting delays of interest are also described, along with a despreading mechanism which saves power by operating only on delays of interest.

BACKGROUND

[0001] Wireless communications is expanding at a phenomenal rate, asmore radio spectrum becomes available for commercial use and as cellularphones become commonplace. For example, in the United States, wirelessphone service is offered both in the cellular (800 MHz) and PCS (1900MHz) bands.

[0002] In addition, there is currently an evolution from analogcommunications to digital communications. Speech is represented by aseries of bits, which are modulated and transmitted from a base stationto a phone. The phone demodulates the received waveform to recover thebits, which are then converted back into speech. There is also a growingdemand for data services, such as e-mail and Internet access, whichrequire digital communications.

[0003] There are many types of digital communications systems.Traditionally, frequency-division-multiple-access (FDMA) is used todivide the spectrum up into a plurality of radio channels correspondingto different carrier frequencies. These carriers may be further dividedinto time slots, referred to as time-division-multiple-access (TDMA), asis done in the D-AMPS, PDC, and GSM digital cellular systems.Alternatively, if the radio channel is wide enough, multiple users canuse the same channel using spread spectrum techniques andcode-division-multiple-access (CDMA).

[0004] Direct-sequence (DS) spread-spectrum modulation is commonly usedin CDMA systems, in which each information symbol is represented by anumber of “chips.” Representing one symbol by many chips gives rise to“spreading,” as the latter typically requires more bandwidth totransmit. The sequence of chips is referred to as the spreading code orsignature sequence. At the receiver, the received signal is despreadusing a despreading code, which is typically the conjugate of thespreading code. IS-95 and J-STD-008 are examples of DS CDMA standards.

[0005] With DS CDMA systems, coherent Rake reception is commonly used.The received signal is despread by correlating to the chip sequence, andthe despread value is weighted by the conjugate of a channel coefficientestimate, removing the phase rotation of the channel and weighting theamplitude to indicate a soft or confidence value. When multipathpropagation is present, the amplitude can vary dramatically. Multipathpropagation can also lead to time dispersion, which causes multiple,resolvable echoes of the signal to be received. Correlators are alignedwith the different echoes. Once the despread values have been weighted,they are summed. This weighting and summing operation is commonlyreferred to as Rake combining.

[0006] A typical digital communications system 100 is shown in FIG. 1.Digital symbols are provided to transmitter 101, which maps the symbolsinto a representation appropriate for the transmission medium or channel(e.g. radio channel) and couples the signal to the transmission mediumvia antenna 102. The transmitter signal passes through channel 103 andis received at antenna 104. The received signal is passed to receiver105. The receiver 105 includes a radio processor 106, a baseband signalprocessor 110, and a post processing unit 112.

[0007] The radio processor tunes to the desired band and desired carrierfrequency, then amplifies, mixes, and filters the signal down tobaseband. At some point, the signal is sampled and quantized, ultimatelyproviding a sequence of baseband received samples. Since the originalradio signal has in-phase (I) and quadrature (Q) components, thebaseband samples typically and I and Q components, giving rise tocomplex, baseband samples.

[0008] The baseband processor 110 is used to detect the digital symbolsthat were transmitted. It may produce soft information as well, whichgives information regarding the likelihood of the detected symbolvalues.

[0009] The post processing unit 112 performs functions that dependhighly on the particular communications application. For example, it mayuse the soft detected values to perform forward error correctiondecoding or error detection decoding. It may convert digital symbolsinto speech using a speech decoder.

[0010] Coherent detection requires estimation of how the symbols weremodified by the transmitter, channel, and/or radio processor. Asdiscussed previously, the transmission medium introduces phase andamplitude changes in signal, as a result of multipath propagation. Thesignal may also have become dispersed, giving rise to signal echoes,each echo having a phase and amplitude associated with it, representedby a complex channel coefficient. Each echo also has a delay associatedwith it. Coherent demodulation requires estimation of these delays andcoefficients. Typically, the channel is modeled as discrete rays, withchannel coefficients assigned to the different delays.

[0011] A conventional baseband processor, 200, is illustrated in FIG. 2.This is the standard baseband processor in a typical, coherent Rakereceiver. The baseband signal is provided to a bank of correlators 202,which correlate different delays of the received signal to thedespreading code, producing correlations, also referred to as despreadvalues. The delays are provided by channel delay estimator 204, whichuses known methods to estimate the delays, such as finding delays whichgive large despread values. The despread values corresponding todifferent delays are combined in combiner 206 using a weighted sum. Theweights are the conjugates of channel coefficient estimates provided bychannel coefficient estimator 208. For example, correlations to a pilotsignal can be used to obtain channel coefficients.

[0012] Consider a simple example, in which the received chip-spacedbaseband samples during one symbol period are represented by r(k). Thesesamples are modeled as:

r(k)=bc ₀ s(k)+bc ₁ s(k−1)+w(k)  (1)

[0013] where b is the symbol sent, c₀ and c₁ are the channelcoefficients, the delays are 0 and 1 chip period, s(k) is the chipsequence used to spread the symbol, and w(k) is a sequence of impairment(noise+interference) samples.

[0014] The bank of correlators produces two despread values, denoted x₀and x₁, corresponding to the two rays. These can be expressed as:$\begin{matrix}{x_{0} = {\frac{1}{L}{\sum\limits_{k = 0}^{L - 1}\quad {{s^{*}(k)}{r(k)}}}}} & (2)\end{matrix}$

[0015] where superscript “*” denotes complex conjugation and L is thedespreading factor. Division of L is shown for illustrative purposes,while in practice it is well known how to extend results to the casewhen the division is omitted. $\begin{matrix}{x_{1} = {\frac{1}{L}{\sum\limits_{k = 0}^{L - 1}\quad {{s^{*}(k)}{r\left( {k + 1} \right)}}}}} & (3)\end{matrix}$

[0016] The combiner combines the two despread values using estimates ofthe channel coefficients, denoted ĉ₀ and ĉ₁, to produce a detectionstatistic that corresponds to an information symbol. This can beexpressed as

z=ĉ ₀ *x ₀ +ĉ ₁ *x ₁  (4)

[0017] The symbol value that is closest to z gives the detected value{circumflex over (b)}. For BPSK modulation, b is either +1 or −1, sothat the detected value is given by the sign of z.

[0018] Channel coefficients can be estimated separately using standardapproaches. For example, with least mean square (LMS) estimation of c₀,one would form the time varying estimate ĉ₀(n), where n is an indexdenoting symbol period, using

ĉ ₀(n+1)=ĉ ₀(n)+μ{circumflex over (b)}*(n)(x ₀(n)−ĉ ₀(n){circumflex over(b)}(n))  (5)

[0019] where μ is the LMS step size. Also {circumflex over (b)} is thedetected symbol value.

[0020] It can be shown that the conventional, coherent Rake receiver isoptimal when the impairment samples are uncorrelated. However, forcellular communication systems, the impairment includes interferencefrom one's own base station as well as interference from other basestations. This interference is typically noise-like at the transmitter.However, at the receiver, the interference has passed through dispersivechannels, which introduce correlation. Thus, for cellular systems, theimpairment samples are correlated and the conventional Rake receiver isno longer optimal, see, for example, Bottomley, “Optimizing the Rakereceiver for the CDMA downlink,” Proc. 43^(rd) IEEE Veh. Technol. Conf.(VTC '93), Secaucus, N.J., May 18-20, 1993.

[0021] Approaches which solve this problem are given in U.S. Pat. No.5,572,552 to Dent et al. Consider combining weight formation. First IIRfiltering approaches are given, in which detection statistics are formedusing a weighted combination of despread values and a weightedcombination of other detection statistics. Second, a FIR approach isgiven. Both IIR and FIR approaches rely on estimating the channelresponses from each base station to the receiver as well as noise andinterference power levels. This requires multiple estimation processesthat increase complexity. Finally, a purely adaptive scheme is given, inwhich the combining weights are tracked directly using decisionfeedback. However, such approaches take time to converge and don'tnecessarily track variations well. Thus, there is a need for improvedcombining weight computation.

[0022] Next, consider delay estimation or correlator placement. In theaforementioned patent, an SNR criterion is used for tap placement thatdepends on channel response, noise power, and interference powerestimates. Again, many quantities must be estimated, increasingcomplexity. Thus, there is a need for a lower complexity approach tocorrelator placement.

SUMMARY OF THE INVENTION

[0023] The aforementioned problems are solved by the present inventionby employing an adaptive whitening operation between the Rake fingers,so that interference that has been colored by a dispersive channel canbe cancelled. The approach estimates fewer quantities than pastapproaches, lumping noise and interference into one impairment process,whose correlation across tap locations is estimated.

BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The features and advantages of the invention will be understoodby reading the description in conjunction with the drawings, in which:

[0025]FIG. 1 is a general digital communications system;

[0026]FIG. 2 is a baseband processor according to the prior art;

[0027]FIG. 3 is a baseband processor according to the present invention;

[0028]FIG. 4 is a process for correlator placement estimation accordingto the present invention;

[0029]FIG. 5 is a metric computation process for use in correlatorplacement estimation according to the present invention; and

[0030]FIG. 6 is an inhibit-controlled sliding correlator according tothe present invention.

DETAILED DESCRIPTION

[0031] For wireless communications, the transmitter emitselectromagnetic waveforms from an antenna, the medium is the radiopropagation environment, and the receiver employs one or more antennasto recover the transmitted signal. While the present invention isdescribed in the context of radio communications, it is not limited tosuch systems. It is also applicable to wireline communications andmagnetic storage systems. In such applications the radio processor isgeneralized to a device that extracts data from the transmission orstorage medium.

[0032] In the present invention, the receiver exploits the fact that, ingeneral, the impairment (noise plus interference) on the differentcorrelators is correlated. This correlation is estimated and used in thecombining process. As a result, redundant interference components areremoved. Implicitly, a whitening operation occurs, which cancelsredundant interference components.

[0033] A baseband processor according to the present invention, 300, isillustrated in FIG. 3. Throughout, like reference numerals are used torefer to like elements. The baseband signal is provided to a bank ofcorrelators 202, which correlates different delays of the receivedsignal to the spreading code. The delays are provided by correlatorplacement estimator 304. The despread values corresponding to differentdelays are combined in modified combiner 306. The modified combiner 306uses channel coefficient estimates from channel coefficient estimator208 and impairment correlation estimates from impairment correlationestimator 310 to combine the despread values to form a detected symboloutput.

[0034] The impairment correlation estimator 310 estimates correlationbetween the impairment on the different correlator outputs. In the past,impairment correlation among different antenna signals has been usedwith MLSE reception to cancel interference, as disclosed in U.S. Pat.No. 5,680,419 to Bottomley, which is incorporated herein by reference.With the present invention, correlation among different despread valuesfrom the same antenna signal is exploited in a Rake receiver.

[0035] The approach for estimating the impairment correlation can besimilar to the approaches given in the Bottomley patent, except thatdespread values from the same antenna are used instead of receivedsamples from different antennas. For example, error signals for thedifferent correlators can be used. For the two-ray example, these errorsignals would be:

e ₀ =x ₀ −{circumflex over (b)}ĉ ₀  (6)

e ₁ =x ₁ −{circumflex over (b)}ĉ ₁  (7)

[0036] where {circumflex over (b)} is a detected symbol value. If thereare pilot symbols, known values can be used. If there is a pilotchannel, there is an effective symbol value, usually +1.

[0037] Collecting the error signals into a vector e(n), where n denotessymbol period, then an impairment correlation matrix estimate{circumflex over (R)}(n) can be updated using:

{circumflex over (R)}(n)=λ{circumflex over (R)}(n−1)+e(n)e ^(H)(n)  (8)

[0038] where superscript “H” denotes Hermitian transpose. Note that{circumflex over (R)}(n) is Hermitian, i.e. {circumflex over(R)}^(H)={circumflex over (R)}, so that only the diagonal and one of theoff diagonal triangles (upper or lower) need be estimated and updated.For the two-ray example, the R matrix has the form: $\begin{matrix}{\hat{R} = \begin{bmatrix}\rho_{00} & \rho_{01} \\\rho_{01}^{*} & \rho_{11}\end{bmatrix}} & (9)\end{matrix}$

[0039] where ρ₀₀ and ρ₁₁ are real numbers (imaginary part is zero). Notethat the inverse of this matrix is used in combining. The inverse can beupdated directly using the well known matrix inversion lemma. The term“impairment correlation” is used to refer not only to correlations, butto any related quantities, such as an inverse correlation matrix.

[0040] The modified combiner 306 would then combine the despread valuesusing both the channel co-efficients and the impairment correlations toproduce a detection statistic. The combining operation can be expressedas:

z ^(/) =ĉ ^(H) {circumflex over (R)} ⁻¹ x  (10)

[0041] where the channel coefficients have also been collected into avector. The detection statistic can be further processed to produce adetected symbol value. It can also be used as a soft value for furtherprocessing.

[0042] The combining can be implemented in a number of ways. Thedespread values can be combined by the impairment correlation matrixfirst, then combined by the channel coefficients. Alternatively, thechannel coefficients and the impairment correlation can be pre-combinedto form weights:

w={circumflex over (R)} ⁻¹ ĉ  (11)

[0043] Then the detection statistic can be expressed as:

z ^(/) =w ^(H) x  (12)

[0044] Alternatively, combining with the impairment correlation matrixestimate and the channel coefficient estimates can be done together. Forthe two-ray example, this can be expressed as:

[0045] $\begin{matrix}{z^{\prime} = {\frac{1}{\hat{R}}\left\lbrack {{\left( {{\rho_{11}{\hat{c}}_{0}^{*}} - {\rho_{01}^{*}{\hat{c}}_{1}^{*}}} \right)x_{0}} + {\left( {{\rho_{00}{\hat{c}}_{1}^{*}} - {\rho_{01}{\hat{c}}_{0}^{*}}} \right)x_{1}}} \right\rbrack}} & (13)\end{matrix}$

[0046] where

|{circumflex over (R)}|=ρ ₀₀ρ₁₁−|ρ₀₁|²  (14)

[0047] Also, it is possible to factorize the inverse impairmentcorrelation matrix estimate using square-root factorization, giving twofactors:

{circumflex over (R)} ⁻¹ =Q ^(H) Q  (15)

[0048] Thus, the combining operation can be expressed as:

z ^(/) =g ^(H) y  (16 )

[0049] where

y=Qx  (17)

[0050] and

g=Qĉ  (18)

[0051] Multiplying the despread vector by Q whitens the impairment, butchanges the overall channel response. As a result, the channelcoefficients have to be modified as well.

[0052] Based on square-root Kalman filtering, it is possible to estimateand track the square-root matrix Q, which is another form of impairmentcorrelation. Then, the despread values used for detection and thedespread values used for channel coefficient estimation can be whitenedfirst. Standard channel coefficient estimation applied to the whiteneddespread values will yield g. Alternatively, one can track the channel,then apply the square root to the channel coefficient estimates, asshown above.

[0053] The operation of the correlator placement unit 304 can employ anyconventional delay estimation approach. For example, unit 304 can employthe approaches disclosed in pending U.S. patent application Ser. No.09/005,580, entitled “Multiple Delay Estimation for Direct SequenceSpread Spectrum Systems” filed on Jan. 12, 1998, which is incorporatedherein by reference, in its entirety. However, the correlator placementunit 304 can alternatively employ a different approach, based onmodifying the approaches in the aforementioned pending application,e.g., modifying the metric to include an estimate of the impairmentcorrelation matrix.

[0054] For example, one of the approaches is illustrated in FIG. 4. Theprocess starts in start block 402. Then despread values corresponding todifferent delays are generated and stored in step 404. Then, at step406, hypothesized tap locations or delays are made. In step 408, ametric is then calculated corresponding to this set of delays. In step410, the metric is compared to previous ones. If the metric is better,it is stored as the new best metric and the corresponding delaycombination is also stored. Then, in step 412, it is determined whetherall the delay combinations have been exhausted. If not, the nextcombination is considered in step 406. Otherwise, the delay estimatesare taken to be the ones corresponding to the best metric and theprocess ends in step 418.

[0055] The key distinction is how the metric is calculated. The metriccalculation step 408 is further detailed in FIG. 5. For the delaycombination, a set of channel coefficients are estimated using standardapproaches in step 502. These channel coefficients should be the“composite” channel coefficients, corresponding to the transmit, medium,and receive filter responses. Side information in the form of transmitand/or receive filter response knowledge can be used to improveestimation, as discussed in the aforementioned pending U.S. PatentApplication to Sourour et al. Then, in step 504, the impairmentcorrelation is estimated using the approaches described previously.Finally, in step 506, the metric is computed using both the channelcoefficient estimates and the impairment correlation estimates. Thepreferred metric can be expressed as:

J=ĉ ^(H) {circumflex over (R)} ⁻¹ ĉ  (19)

[0056] which corresponds to an SNR figure of merit.

[0057] Similarly, impairment correlation among different antenna signalshas been used with synchronization, as disclosed in pending applicationU.S. patent application Ser. No. 08/773,560 to Bottomley andChennakeshu, filed Dec. 27, 1996, which is incorporated here in itsentirety by reference. With the present invention, correlation amongdifferent despread values form the same antenna signal is exploited todetermine correlator or “finger” placement in a Rake receiver.

[0058] It may be advantageous to perform conventional delay estimationfirst, then consider delays within a certain proximity of theconventional delay estimates. It may also be desirable to keep the Mstrongest ray delay estimates, then only consider alternatives for theremaining P delay estimates.

[0059] Referring back to FIG. 3, the bank of correlators 202 can berealized in a number of ways. It can be a group of integrate-and-dumpcorrelators. It can also be realized using a single sliding correlator.In this case, delays associated with the bank of correlators correspondto selecting which of the outputs of the sliding correlator are kept forfurther processing. A third approach is to use a selectively inhibitedsliding correlator as illustrated in FIG. 6.

[0060] Data samples are provided to delay line 602, which includes delayelements 604 a-604 c. For this example, it is assumed that the samplingrate is two samples per chip and that the despreading code section haslength three. It will be apparent to one skilled in the art that thisaspect of the present invention may be extended to any sampling rate andany despreading length. Also note that delay 604 a may be omitted,depending on how the input samples are generated.

[0061] The delayed samples are provided to processing engine 606, whichincludes remove chip units 608 a-608 c and adder 610. The delayedsamples are provided to remove chip units 608 a-608 c, where thedespreading chip values are removed from the samples, producing modifiedsamples. For example, the received sample is multiplied by the conjugateof the despreading chip value. When chip values are +1 or −1, then chipremoval is simply negating or not negating the received sample. Themodified samples are added together in adder 610, to produce despreadvalues.

[0062] What distinguishes the operation of the processing engine 606from a conventional sliding correlator is that the operation of theelements in the processing engine 606 can be selectively inhibited,saving power when despread values are not needed. Thus, remove chipunits 608 a-608 c and adder 610 have a control input, which determineswhether an operation will be performed or not.

[0063] The processing engine 606 is controlled by inhibit unit 612,which produces the control signal based on the delays to be used. Inessence, the inhibit control unit 612 instructs the processing engine606 to produce despread values only for those delays of interest. Forall other delays, the inhibit control unit 612 instructs the processingengine 606 not to process the delayed samples.

[0064] The delay line 602 can be efficiently implemented as a circularbuffer. This avoids the power consuming need to repeatedly shift datasamples.

[0065] This approach overcomes the problem that a bank of 4integrate-and-dump correlators can only examine up to 4 delays. It alsoovercomes the problem that a sliding correlator has, which is largepower consumption. The inhibit-controlled sliding correlator can be usedwith conventional Rake combining as well as for initial acquisition anddelay tracking.

[0066] Referring back to FIG. 3, other forms of correlation combiningcan be used, based on known antenna array processing approaches. Forexample, the impairment correlation estimate can be replaced by adespread correlation estimate, in which the error signal e is replacedwith x when forming the {circumflex over (R)} matrix. This approach willalso cancel interference, though the “soft” detection statistic will notwork as well in subsequent processing as the preferred embodimentalready given.

[0067] The present invention can use any type of channel coefficienttracking algorithm. For example, the LMS, KLMS (see, e.g., Jamal et al.,“Adaptive MLSE performance on the D-AMPS 1900 channel,” IEEE Trans. Veh.Technol., vol. 46, pp. 634-641, August 1997), RLS, and Kalman trackingalgorithms are appropriate. While chip-spaced rays were used asexamples, the rays can have arbitrary spacing, including fractionalspacing. Channel coefficient estimation can also be done usinginterpolation between pilot symbol sections. Similarly, the presentinvention can use a number of approaches to impairment correlationestimation. The impairment correlation can be either tracked orinterpolated between pilot symbol sections. When receiver quantities aretracked, per survivor processing (PSP) can be used to improveperformance, by keeping channel coefficient estimates and impairmentcorrelation estimates per hypothesized symbol values.

[0068] A modified approach is possible, in which the impairmentcorrelation estimate used to combine the despread values is acombination of the impairment correlation estimate and a fixed value.This provides a way of gracefully switching between conventionalapproaches (the fixed value is the identity matrix) and the presentinvention. It can also be used to switch between an adaptive estimateand a known structure. For example, if the interference isnondispersive, then the impairment is colored only by the receivefilter. Thus, the fixed matrix could be a matrix of pulse shapeautocorrelation values, possibly scaled by an estimate of the noisepower. The “fixed matrix” could also be adaptive, using a pulse shapeautocorrelation matrix scaled by an adaptive noise power estimate.

[0069] The present invention can be used in a multi-pass approach.Despread values corresponding to a data frame can be stored. In thepost-processing phase, forward-error-correction andforward-error-detection decoding can be used to correct or detecterrors. Then, re-encoding can be used to provide reference symbols for asecond-pass, for better parameter estimation. Multi-pass demodulation isdescribed in U.S. Pat. No. 5,673,291 to Dent which is incorporatedherein by reference.

[0070] The present invention can also be used in conjunction withmultiple receive antennas. In pending U.S. patent application Ser. No.08/992,174 to Jonas Karlsson and Sara Mazur, entitled “Code divisionmultiple access mobile station interference suppression”, multipleantenna despread values corresponding to a particular delay are combinedusing an impairment estimate across antennas. With the presentinvention, the despread values from all antennas would be collectedtogether into one set of despread values, which would be combinedaccording to the present invention. Thus, impairment correlation acrossantennas and across delays would be estimated and used in combining.

[0071] A hybrid approach is possible, in which groups of despread valuesare combined using the present invention, and those groups are thensimply added together to form the detection statistic. Unlike theaforementioned application to Karlsson et al., the groups do notnecessarily have to correspond to the same delay, but differentantennas.

[0072] The invention has been described in the context of a single,modulated traffic channel. However, the invention is also applicable tosystems with pilot symbols or with a pilot channel, such as the IS-95downlink. With pilot symbols, the symbol values are known, so thatchannel coefficient estimation and impairment estimation can use known,instead of detected, symbol values. With a pilot channel, the pilotchannel can be viewed as a continuous sequence of known symbol values(usually all +1). Thus, these known symbol values could be used.

[0073] Those skilled in the art will appreciate that the presentinvention is not limited to the specific embodiments which have beendescribed herein for the purposes of illustration. The scope of theinvention, therefore, is defined by the claims which are appendedhereto, rather than the foregoing description, and all equivalents whichare consistent with the meaning of the claims are intended to beembraced herein.

What is claimed is:
 1. A method for producing detection statisticscorresponding to information symbols comprising: receiving a signal andcreating data samples therefrom; correlating said data samples to a codeto produce despread values; estimating a channel response to producechannel coefficient estimates; estimating impairment correlation amongdifferent delays of said received signal to produce impairmentcorrelation estimates; and combining said despread values to produce adetection statistic using the channel coefficient estimates and theimpairment correlation estimates.
 2. The method of claim 1 wherein thestep of estimating a channel response further comprises the step of:correlating to a pilot channel to produce pilot channel despread values.3. The method of claim 1 wherein the step of estimating a channelresponse further comprises the step of: correlating to pilot symbols toproduce pilot symbol despread values.
 4. The method of claim 1 whereinthe step of estimating a channel response includes receiving symbolvalues.
 5. The method of claim 4 wherein the symbol values correspond topilot symbol values.
 6. The method of claim 4 wherein the symbol valuescorrespond to detected symbol values.
 7. The method of claim 4 whereinthe symbol values correspond to re-encoded symbol values.
 8. The methodof claim 1 wherein the step of estimating impairment correlation furthercomprises the step of: correlating to a pilot channel to produce pilotchannel despread values.
 9. The method of claim 1 wherein the step ofestimating impairment correlation further comprises the step of:correlating to pilot symbols to produce pilot symbol despread values.10. The method of claim 1 wherein the step of estimating impairmentcorrelation further comprises the step of: receiving symbol values. 11.The method of claim 10 wherein the symbol values correspond to pilotsymbol values.
 12. The method of claim 10 wherein the symbol valuescorrespond to detected symbol values.
 13. The method of claim 10 whereinthe symbol values correspond to re-encoded symbol values.
 14. The methodof claim 1 wherein the step of estimating impairment correlation amongdifferent delays further comprises the step of: receiving channelcoefficient estimates.
 15. The method of claim 1 wherein the step ofcombining despread values further comprises the steps of: combining saidchannel coefficient estimates and said impairment correlation estimatesto produce weights; and combining said weights and said despread valuesto produce said detection statistic.
 16. The method of claim 1 whereinsaid step of combining despread values further comprises the step of:combining said despread values and said impairment correlation estimatesto produce modified despread values; and combining said channelcoefficient estimates and said modified despread values to produce adetection statistic.
 17. The method of claim 1 wherein said despreadvalues are combined with said impairment correlation estimates toproduce whitened despread values.
 18. The method of claim 1 wherein saidstep of estimating impairment correlation among different delays furthercomprises the step of: estimating elements of an inverse impairmentcorrelation matrix.
 19. A spread spectrum receiver comprising: means forreceiving a signal and creating data samples therefrom; means forcorrelating said data samples to a code to produce despread values;means for estimating a channel response to produce channel coefficientestimates; means for estimating impairment correlation among differentdelays of said received signal to produce impairment correlationestimates; and means for combining said despread values to produce adetection statistic using the channel coefficient estimates and theimpairment correlation estimates.
 20. The receiver of claim 19 whereinsaid means for estimating a channel response further comprises: meansfor correlating to a pilot channel to produce pilot channel despreadvalues.
 21. The receiver of claim 19 wherein the means for estimating achannel response further comprises: means for correlating to pilotsymbols to produce pilot symbol despread values.
 22. The receiver ofclaim 19 wherein the means for estimating a channel response furthercomprises means for receiving symbol values.
 23. The receiver of claim22 wherein the symbol values correspond to pilot symbol values.
 24. Thereceiver of claim 22 wherein the symbol values correspond to detectedsymbol values.
 25. The receiver of claim 22 wherein the symbol valuescorrespond to re-encoded symbol values.
 26. The receiver of claim 19wherein the means for estimating impairment correlation furthercomprises: means for correlating to a pilot channel to produce pilotchannel despread values.
 27. The receiver of claim 19 wherein the meansfor estimating impairment correlation further comprises: means forcorrelating to pilot symbols to produce pilot symbol despread values.28. The receiver of claim 19 wherein the means for estimating impairmentcorrelation further comprises: means for receiving symbol values. 29.The receiver of claim 28 wherein the symbol values correspond to pilotsymbol values.
 30. The receiver of claim 28 wherein the symbol valuescorrespond to detected symbol values.
 31. The receiver of claim 28wherein the symbol values correspond to re-encoded symbol values. 32.The receiver of claim 19 wherein the means for estimating impairmentcorrelation among different delays further comprises: means forreceiving channel coefficient estimates.
 33. The receiver of claim 19wherein the means for combining despread values further comprises: meansfor combining said channel coefficient estimates and said impairmentcorrelation estimates to produce weights; and means for combining saidweights and said despread values to produce said detection statistic.34. The receiver of claim 19 wherein said means for combining despreadvalues further comprises: means for combining said despread values andsaid impairment correlation estimates to produce modified despreadvalues; and means for combining said channel coefficient estimates andsaid modified despread values to produce a detection statistic.
 35. Thereceiver of claim 19 wherein said despread values are combined with saidimpairment correlation estimates to produce whitened despread values.36. The receiver of claim 19 wherein said means for estimatingimpairment correlation among different delays further comprises: meansfor estimating elements of an inverse impairment correlation matrix. 37.A method for estimating despreading delays comprising the steps of:producing sets of candidate delays; estimating channel responses toproduce channel coefficient estimates corresponding to the sets ofcandidate delays; estimating impairment correlation among delays of thereceived signal corresponding to the candidate delays to produceimpairment correlation estimates; combining the channel coefficientestimates and the impairment correlation estimates to produce metricscorresponding to the sets of candidate delays; and producing estimatesof despreading delays using the metrics.
 38. The method of claim 37,wherein said step of estimating channel responses further comprises thestep of: generating, as said channel coefficient estimates, values whichcorrespond to: transmit filter response characteristics, medium responsecharacteristics and receiver filter characteristics.
 39. The method ofclaim 37, wherein said step of estimating channel responses furthercomprises the step of: improving said channel coefficient estimatesusing knowledge of at least one of transmit and receiver filter responsecharacteristics.
 40. A receiver which uses estimated despreading delaysto process a received signal comprising: means for producing sets ofcandidate delays; means for estimating channel responses to producechannel coefficient estimates corresponding to the sets of candidatedelays; means for estimating impairment correlation among delays of thereceived signal corresponding to the candidate delays to produceimpairment correlation estimates; means for combining the channelcoefficient estimates and the impairment correlation estimates toproduce metrics corresponding to the sets of candidate delays; and meansfor producing estimates of despreading delays using the metrics.
 41. Thereceiver of claim 40, wherein said means for estimating channelresponses further comprises: means for generating, as said channelcoefficient estimates, values which correspond to: transmit filterresponse characteristics, medium response characteristics and receiverfilter characteristics.
 42. The receiver of claim 40, wherein said meansfor estimating channel responses further comprises: means for improvingsaid channel coefficient estimates using knowledge of at least one oftransmit and receiver filter response characteristics.
 43. A method fordespreading a received signal comprising the steps of: storing aplurality of data samples; selectively processing the stored datasamples by combining them with chip values; and summing the result toproduce despread values.
 44. The method of claim 43 wherein said step ofselectively processing further comprises the step of: selectivelycombining said stored data samples with chip values during each sampleperiod.
 45. The method of claim 43, wherein said step of selectivelyprocessing further comprises the step of: selectively combining saidstored data samples only for delays of interest.
 46. A slidingcorrelator comprising: a delay line having a plurality of delay elementsfor delaying a stream of data samples input thereto; a plurality ofremove chip units, each associated with an output of one of saidplurality of delay elements, for removing chips from said delayed datasamples; an adder for receiving outputs from said plurality of removechip units and adding said outputs together; and an inhibit unit forselectively enabling at least one of said plurality of remove chip unitsand said adder.
 47. The sliding correlator of claim 46, wherein saiddelay line is a circular buffer.
 48. The sliding correlator of claim 46,wherein said inhibit unit operates to enable said at least one of saidplurality of remove chip units and said adder only for delays ofinterest.
 49. The sliding correlator of claim 46, wherein said inhibitunit selectively enables and disables said plurality of remove chipunits and said adder together.
 50. The method of claim 1 wherein thestep of receiving a signal further comprises the step of: receiving asignal corresponding to a plurality of antenna signals.
 51. The receiverof claim 19 wherein the means for receiving a signal further comprises:means for receiving signals corresponding to a plurality of antennasignals.